update

app.py

@@ -302,13 +302,16 @@ with gr.Blocks() as demo:
 		stage1_id = uuid.uuid4().hex
 		os.mkdir(rootpath+'/'+stage1_id+'/')
 		
+		inputname = os.path.basename(str(in_loc))
+		outputname = os.path.splitext(inputname)[0]+'_mapping_result.csv'
+		
 		stage1_info = {
 			"filePath" : rootpath+'/'+stage1_id+'/',
 			"fileNames" : {
 				"inputLocation" : in_loc,
 				"inputMontage" : rootpath+'/'+stage1_id+'/input_montage.png',
 		    	"mappedMontage" : rootpath+'/'+stage1_id+'/mapped_montage.png',
-		    	"outputResult" : rootpath+'/'+stage1_id+'/mapping_result.json'
+		    	"outputResult" : rootpath+'/'+stage1_id+'/'+outputname
 			},
 			"state" : "step1-initializing",
 	    	"step2" : {
@@ -799,14 +802,14 @@ with gr.Blocks() as demo:
 		stage2_id = uuid.uuid4().hex
 		os.mkdir(rootpath+'/'+stage2_id+'/')
 		
-		filename = os.path.basename(str(in_data))
-		new_filename = modelname+'_'+os.path.splitext(filename)[0]+'.csv'
+		inputname = os.path.basename(str(in_data))
+		outputname = modelname+'_'+os.path.splitext(inputname)[0]+'.csv'
 		
 		stage2_info = {
 			"filePath" : rootpath+'/'+stage2_id+'/',
 			"fileNames" : {
 				"inputData" : in_data,
-				"outputData" : rootpath+'/'+stage2_id+'/'+new_filename
+				"outputData" : rootpath+'/'+stage2_id+'/'+outputname
 			},
 			"state" : "running",
 			"sampleRate" : int(samplerate)
@@ -822,8 +825,8 @@ with gr.Blocks() as demo:
 		mapping_results = stage1_info["mappingResults"]
 		samplerate = stage2_info["sampleRate"]
 		filepath = stage2_info["filePath"]
-		filename = stage2_info["fileNames"]["inputData"]
-		new_filename = stage2_info["fileNames"]["outputData"]
+		inputname = stage2_info["fileNames"]["inputData"]
+		outputname = stage2_info["fileNames"]["outputData"]
 		
 		break_flag = False
 		for i in range(batch_num):
@@ -838,13 +841,13 @@ with gr.Blocks() as demo:
 				os.mkdir(filepath+'temp_data/')
 				
 				# step1: Reorder input data
-				data_shape = app_utils.reorder_data(new_idx, fill_flags, filename, filepath+'temp_data/'+m_filename)
+				data_shape = app_utils.reorder_data(new_idx, fill_flags, inputname, filepath+'temp_data/'+m_filename)
 				# step2: Data preprocessing
 				total_file_num = utils.preprocessing(filepath+'temp_data/', m_filename, samplerate)
 				# step3: Signal reconstruction
 				utils.reconstruct(modelname, total_file_num, filepath+'temp_data/', d_filename, samplerate)
 				# step4: Restore original order
-				app_utils.restore_order(i, data_shape, new_idx, fill_flags, filepath+'temp_data/'+d_filename, new_filename)
+				app_utils.restore_order(i, data_shape, new_idx, fill_flags, filepath+'temp_data/'+d_filename, outputname)
 			except FileNotFoundError:
 				print('break!!')
 				break_flag = True
@@ -861,7 +864,7 @@ with gr.Blocks() as demo:
 					run_btn : gr.Button(visible=True),
 					cancel_btn : gr.Button(visible=False),
 					batch_md : gr.Markdown(visible=False),
-					out_data_file : gr.File(new_filename, visible=True)}
+					out_data_file : gr.File(outputname, visible=True)}
 	
 	run_btn.click(
 		fn = reset_stage2,

app_utils.py

@@ -10,9 +10,9 @@ from scipy.interpolate import Rbf
 from scipy.optimize import linear_sum_assignment
 from sklearn.neighbors import NearestNeighbors
 
-def reorder_data(idx_order, fill_flags, filename, new_filename):
+def reorder_data(idx_order, fill_flags, inputname, m_filename):
 	# read the input data
-	raw_data = utils.read_train_data(filename)
+	raw_data = utils.read_train_data(inputname)
 	#print(raw_data.shape)
 	new_data = np.zeros((30, raw_data.shape[1]))
 	
@@ -26,23 +26,23 @@ def reorder_data(idx_order, fill_flags, filename, new_filename):
 			tmp_data = [raw_data[j, :] for j in idx_set]
 			new_data[i, :] = np.mean(tmp_data, axis=0)
 	
-	utils.save_data(new_data, new_filename)
+	utils.save_data(new_data, m_filename)
 	return raw_data.shape
 
-def restore_order(batch_cnt, raw_data_shape, idx_order, fill_flags, filename, new_filename):
+def restore_order(batch_cnt, raw_data_shape, idx_order, fill_flags, d_filename, outputname):
 	# read the denoised data
-	d_data = utils.read_train_data(filename)
+	d_data = utils.read_train_data(d_filename)
 	if batch_cnt == 0:
 		new_data = np.zeros((raw_data_shape[0], d_data.shape[1]))
 		#print(new_data.shape)
 	else:
-		new_data = utils.read_train_data(new_filename)
+		new_data = utils.read_train_data(outputname)
 	
 	for i, (idx_set, flag) in enumerate(zip(idx_order, fill_flags)):
 		if flag == False: # ignore if this channel was filled using "fillmode"
 			new_data[idx_set[0], :] = d_data[i, :]
 	
-	utils.save_data(new_data, new_filename)
+	utils.save_data(new_data, outputname)
 	return
 
 def get_matched(tpl_order, tpl_dict):